DeepBeepMeep commited on
Commit
bd5ec3d
·
1 Parent(s): 72e5204

AccVideo support

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Files changed (2) hide show
  1. wan/text2video.py +4 -4
  2. wgp.py +7 -5
wan/text2video.py CHANGED
@@ -470,14 +470,14 @@ class WanT2V:
470
  latent_noise_factor = t / 1000
471
  for zz, zz_r, ll in zip(z, z_reactive, [latents]):
472
  pass
473
- # zz[0:16, ref_images_count:overlapped_latents_size + ref_images_count] = zz_r[:, ref_images_count:] * (1.0 - overlap_noise_factor) + torch.randn_like(zz_r[:, ref_images_count:] ) * overlap_noise_factor
474
- # ll[:, 0:overlapped_latents_size + ref_images_count] = zz_r * (1.0 - latent_noise_factor) + torch.randn_like(zz_r ) * latent_noise_factor
475
 
476
  if conditioning_latents_size > 0 and overlap_noise > 0:
477
  pass
478
  overlap_noise_factor = overlap_noise / 1000
479
- latents[:, conditioning_latents_size + ref_images_count:] = latents[:, conditioning_latents_size + ref_images_count:] * (1.0 - overlap_noise_factor) + torch.randn_like(latents[:, conditioning_latents_size + ref_images_count:]) * overlap_noise_factor
480
- #timestep = [torch.tensor([t.item()] * (conditioning_latents_size + ref_images_count) + [t.item() - overlap_noise]*(len(timesteps) - conditioning_latents_size - ref_images_count))]
481
 
482
  if target_camera != None:
483
  latent_model_input = torch.cat([latents, source_latents], dim=1)
 
470
  latent_noise_factor = t / 1000
471
  for zz, zz_r, ll in zip(z, z_reactive, [latents]):
472
  pass
473
+ zz[0:16, ref_images_count:overlapped_latents_size + ref_images_count] = zz_r[:, ref_images_count:] * (1.0 - overlap_noise_factor) + torch.randn_like(zz_r[:, ref_images_count:] ) * overlap_noise_factor
474
+ ll[:, 0:overlapped_latents_size + ref_images_count] = zz_r * (1.0 - latent_noise_factor) + torch.randn_like(zz_r ) * latent_noise_factor
475
 
476
  if conditioning_latents_size > 0 and overlap_noise > 0:
477
  pass
478
  overlap_noise_factor = overlap_noise / 1000
479
+ # latents[:, conditioning_latents_size + ref_images_count:] = latents[:, conditioning_latents_size + ref_images_count:] * (1.0 - overlap_noise_factor) + torch.randn_like(latents[:, conditioning_latents_size + ref_images_count:]) * overlap_noise_factor
480
+ # timestep = [torch.tensor([t.item()] * (conditioning_latents_size + ref_images_count) + [t.item() - overlap_noise]*(target_shape[1] - conditioning_latents_size - ref_images_count))]
481
 
482
  if target_camera != None:
483
  latent_model_input = torch.cat([latents, source_latents], dim=1)
wgp.py CHANGED
@@ -42,8 +42,8 @@ global_queue_ref = []
42
  AUTOSAVE_FILENAME = "queue.zip"
43
  PROMPT_VARS_MAX = 10
44
 
45
- target_mmgp_version = "3.4.7"
46
- WanGP_version = "5.4"
47
  prompt_enhancer_image_caption_model, prompt_enhancer_image_caption_processor, prompt_enhancer_llm_model, prompt_enhancer_llm_tokenizer = None, None, None, None
48
 
49
  from importlib.metadata import version
@@ -3263,11 +3263,13 @@ def generate_video(
3263
  if exp > 0:
3264
  from rife.inference import temporal_interpolation
3265
  if sliding_window and window_no > 1:
3266
- sample = torch.cat([frames_already_processed[:, -2:-1], sample], dim=1)
 
3267
  sample = temporal_interpolation( os.path.join("ckpts", "flownet.pkl"), sample, exp, device=processing_device)
3268
  sample = sample[:, 1:]
3269
  else:
3270
  sample = temporal_interpolation( os.path.join("ckpts", "flownet.pkl"), sample, exp, device=processing_device)
 
3271
 
3272
  output_fps = output_fps * 2**exp
3273
 
@@ -4843,8 +4845,8 @@ def generate_video_tab(update_form = False, state_dict = None, ui_defaults = Non
4843
  temporal_upsampling = gr.Dropdown(
4844
  choices=[
4845
  ("Disabled", ""),
4846
- ("Rife x2 (32 frames/s)", "rife2"),
4847
- ("Rife x4 (64 frames/s)", "rife4"),
4848
  ],
4849
  value=ui_defaults.get("temporal_upsampling", ""),
4850
  visible=True,
 
42
  AUTOSAVE_FILENAME = "queue.zip"
43
  PROMPT_VARS_MAX = 10
44
 
45
+ target_mmgp_version = "3.4.8"
46
+ WanGP_version = "5.41"
47
  prompt_enhancer_image_caption_model, prompt_enhancer_image_caption_processor, prompt_enhancer_llm_model, prompt_enhancer_llm_tokenizer = None, None, None, None
48
 
49
  from importlib.metadata import version
 
3263
  if exp > 0:
3264
  from rife.inference import temporal_interpolation
3265
  if sliding_window and window_no > 1:
3266
+ sample = torch.cat([previous_before_last_frame, sample], dim=1)
3267
+ previous_before_last_frame = sample[:, -2:-1].clone()
3268
  sample = temporal_interpolation( os.path.join("ckpts", "flownet.pkl"), sample, exp, device=processing_device)
3269
  sample = sample[:, 1:]
3270
  else:
3271
  sample = temporal_interpolation( os.path.join("ckpts", "flownet.pkl"), sample, exp, device=processing_device)
3272
+ previous_before_last_frame = sample[:, -2:-1].clone()
3273
 
3274
  output_fps = output_fps * 2**exp
3275
 
 
4845
  temporal_upsampling = gr.Dropdown(
4846
  choices=[
4847
  ("Disabled", ""),
4848
+ ("Rife x2 frames/s", "rife2"),
4849
+ ("Rife x4 frames/s", "rife4"),
4850
  ],
4851
  value=ui_defaults.get("temporal_upsampling", ""),
4852
  visible=True,